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- Title
Lane Image Detection Based on Convolution Neural Network Multi-Task Learning.
- Authors
Li, Junfeng; Zhang, Dehai; Ma, Yu; Liu, Qing
- Abstract
Based on deep neural network multi-task learning technology, lane image detection is studied to improve the application level of driverless technology, improve assisted driving technology and reduce traffic accidents. The lane line database published by Caltech and Tucson company is used to extract the ROI (Region of Interest), scale, and inverse perspective transformation as well as to preprocess the image, so as to enrich the data set and improve the efficiency of the algorithm. In this study, ZFNet is used to replace the basic networks of VPGNet, and their structures are changed to improve the detection efficiency. Multi-label classification, grid box regression and object mask are used as three task modules to build a multi-task learning network named ZF-VPGNet. Considering that neural networks will be combined with embedded systems in the future, the network will be compressed to CZF-VPGNet without excessively affecting the accuracy. Experimental results show that the vision system of driverless technology in this study achieved good test results. In the case of fuzzy lane line and missing lane line mark, the improved algorithm can still detect and obtain the correct results, and achieves high accuracy and robustness. CZF-VPGNet can achieve high real-time performance (26FPS), and a single forward pass takes about 36 ms or less.
- Subjects
TUCSON (Ariz.); CONVOLUTIONAL neural networks; SIGNAL convolution; DEEP learning; ALGORITHMS; TRAFFIC accidents
- Publication
Electronics (2079-9292), 2021, Vol 10, Issue 19, p2356
- ISSN
2079-9292
- Publication type
Article
- DOI
10.3390/electronics10192356